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<li class="navelem"><a class="el" href="../../d9/df8/tutorial_root.html">OpenCV Tutorials</a></li><li class="navelem"><a class="el" href="../../d7/da8/tutorial_table_of_content_imgproc.html">Image Processing (imgproc module)</a></li>  </ul>
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<div class="title">Hough Line Transform </div>  </div>
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<div class="contents">
<div class="textblock"><p><b>Prev Tutorial:</b> <a class="el" href="../../da/d5c/tutorial_canny_detector.html">Canny Edge Detector</a></p>
<p><b>Next Tutorial:</b> <a class="el" href="../../d4/d70/tutorial_hough_circle.html">Hough Circle Transform</a></p>
<table class="doxtable">
<tr>
<th align="right"></th><th align="left"></th></tr>
<tr>
<td align="right">Original author </td><td align="left">Ana Huamán </td></tr>
<tr>
<td align="right">Compatibility </td><td align="left">OpenCV &gt;= 3.0 </td></tr>
</table>
<h2>Goal </h2>
<p>In this tutorial you will learn how to:</p>
<ul>
<li>Use the OpenCV functions <b><a class="el" href="../../dd/d1a/group__imgproc__feature.html#ga46b4e588934f6c8dfd509cc6e0e4545a" title="Finds lines in a binary image using the standard Hough transform. ">HoughLines()</a></b> and <b><a class="el" href="../../dd/d1a/group__imgproc__feature.html#ga8618180a5948286384e3b7ca02f6feeb" title="Finds line segments in a binary image using the probabilistic Hough transform. ">HoughLinesP()</a></b> to detect lines in an image.</li>
</ul>
<h2>Theory </h2>
<dl class="section note"><dt>Note</dt><dd>The explanation below belongs to the book <b>Learning OpenCV</b> by Bradski and Kaehler.</dd></dl>
<h2>Hough Line Transform </h2>
<ol type="1">
<li>The Hough Line Transform is a transform used to detect straight lines.</li>
<li>To apply the Transform, first an edge detection pre-processing is desirable.</li>
</ol>
<h3>How does it work?</h3>
<ol type="1">
<li><p class="startli">As you know, a line in the image space can be expressed with two variables. For example:</p><ol type="a">
<li>In the <b>Cartesian coordinate system:</b> Parameters: \((m,b)\).</li>
<li>In the <b>Polar coordinate system:</b> Parameters: \((r,\theta)\)</li>
</ol>
<div class="image">
<img src="../../Hough_Lines_Tutorial_Theory_0.jpg" alt="Hough_Lines_Tutorial_Theory_0.jpg"/>
</div>
<p class="startli">For Hough Transforms, we will express lines in the <em>Polar system</em>. Hence, a line equation can be written as:</p>
<p class="formulaDsp">
\[y = \left ( -\dfrac{\cos \theta}{\sin \theta} \right ) x + \left ( \dfrac{r}{\sin \theta} \right )\]
</p>
</li>
</ol>
<p>Arranging the terms: \(r = x \cos \theta + y \sin \theta\)</p>
<ol type="1">
<li><p class="startli">In general for each point \((x_{0}, y_{0})\), we can define the family of lines that goes through that point as:</p>
<p class="formulaDsp">
\[r_{\theta} = x_{0} \cdot \cos \theta + y_{0} \cdot \sin \theta\]
</p>
<p class="startli">Meaning that each pair \((r_{\theta},\theta)\) represents each line that passes by \((x_{0}, y_{0})\).</p>
</li>
<li><p class="startli">If for a given \((x_{0}, y_{0})\) we plot the family of lines that goes through it, we get a sinusoid. For instance, for \(x_{0} = 8\) and \(y_{0} = 6\) we get the following plot (in a plane \(\theta\) - \(r\)):</p>
<div class="image">
<img src="../../Hough_Lines_Tutorial_Theory_1.jpg" alt="Hough_Lines_Tutorial_Theory_1.jpg"/>
</div>
<p class="startli">We consider only points such that \(r &gt; 0\) and \(0&lt; \theta &lt; 2 \pi\).</p>
</li>
<li><p class="startli">We can do the same operation above for all the points in an image. If the curves of two different points intersect in the plane \(\theta\) - \(r\), that means that both points belong to a same line. For instance, following with the example above and drawing the plot for two more points: \(x_{1} = 4\), \(y_{1} = 9\) and \(x_{2} = 12\), \(y_{2} = 3\), we get:</p>
<div class="image">
<img src="../../Hough_Lines_Tutorial_Theory_2.jpg" alt="Hough_Lines_Tutorial_Theory_2.jpg"/>
</div>
<p class="startli">The three plots intersect in one single point \((0.925, 9.6)\), these coordinates are the parameters ( \(\theta, r\)) or the line in which \((x_{0}, y_{0})\), \((x_{1}, y_{1})\) and \((x_{2}, y_{2})\) lay.</p>
</li>
<li>What does all the stuff above mean? It means that in general, a line can be <em>detected</em> by finding the number of intersections between curves.The more curves intersecting means that the line represented by that intersection have more points. In general, we can define a <em>threshold</em> of the minimum number of intersections needed to <em>detect</em> a line.</li>
<li>This is what the Hough Line Transform does. It keeps track of the intersection between curves of every point in the image. If the number of intersections is above some <em>threshold</em>, then it declares it as a line with the parameters \((\theta, r_{\theta})\) of the intersection point.</li>
</ol>
<h3>Standard and Probabilistic Hough Line Transform</h3>
<p>OpenCV implements two kind of Hough Line Transforms:</p>
<p>a. <b>The Standard Hough Transform</b></p>
<ul>
<li>It consists in pretty much what we just explained in the previous section. It gives you as result a vector of couples \((\theta, r_{\theta})\)</li>
<li>In OpenCV it is implemented with the function <b><a class="el" href="../../dd/d1a/group__imgproc__feature.html#ga46b4e588934f6c8dfd509cc6e0e4545a" title="Finds lines in a binary image using the standard Hough transform. ">HoughLines()</a></b></li>
</ul>
<p>b. <b>The Probabilistic Hough Line Transform</b></p>
<ul>
<li>A more efficient implementation of the Hough Line Transform. It gives as output the extremes of the detected lines \((x_{0}, y_{0}, x_{1}, y_{1})\)</li>
<li>In OpenCV it is implemented with the function <b><a class="el" href="../../dd/d1a/group__imgproc__feature.html#ga8618180a5948286384e3b7ca02f6feeb" title="Finds line segments in a binary image using the probabilistic Hough transform. ">HoughLinesP()</a></b></li>
</ul>
<h3>What does this program do?</h3>
<ul>
<li>Loads an image</li>
<li>Applies a <em>Standard Hough Line Transform</em> and a <em>Probabilistic Line Transform</em>.</li>
<li>Display the original image and the detected line in three windows.</li>
</ul>
<h2>Code </h2>
 <div class='newInnerHTML' title='cpp' style='display: none;'>C++</div><div class='toggleable_div label_cpp' style='display: none;'><p> The sample code that we will explain can be downloaded from <a href="https://raw.githubusercontent.com/opencv/opencv/master/samples/cpp/tutorial_code/ImgTrans/houghlines.cpp">here</a>. A slightly fancier version (which shows both Hough standard and probabilistic with trackbars for changing the threshold values) can be found <a href="https://raw.githubusercontent.com/opencv/opencv/master/samples/cpp/tutorial_code/ImgTrans/HoughLines_Demo.cpp">here</a>. </p><div class="fragment"><div class="line"></div><div class="line"><span class="preprocessor">#include &quot;<a class="code" href="../../d6/d87/imgcodecs_8hpp.html">opencv2/imgcodecs.hpp</a>&quot;</span></div><div class="line"><span class="preprocessor">#include &quot;<a class="code" href="../../d4/dd5/highgui_8hpp.html">opencv2/highgui.hpp</a>&quot;</span></div><div class="line"><span class="preprocessor">#include &quot;<a class="code" href="../../d1/d4f/imgproc_2include_2opencv2_2imgproc_8hpp.html">opencv2/imgproc.hpp</a>&quot;</span></div><div class="line"></div><div class="line"><span class="keyword">using namespace </span><a class="code" href="../../d2/d75/namespacecv.html">cv</a>;</div><div class="line"><span class="keyword">using namespace </span>std;</div><div class="line"></div><div class="line"><span class="keywordtype">int</span> main(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span>** argv)</div><div class="line">{</div><div class="line">    <span class="comment">// Declare the output variables</span></div><div class="line">    <a class="code" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> dst, cdst, cdstP;</div><div class="line"></div><div class="line">    <span class="keyword">const</span> <span class="keywordtype">char</span>* default_file = <span class="stringliteral">&quot;sudoku.png&quot;</span>;</div><div class="line">    <span class="keyword">const</span> <span class="keywordtype">char</span>* filename = argc &gt;=2 ? argv[1] : default_file;</div><div class="line"></div><div class="line">    <span class="comment">// Loads an image</span></div><div class="line">    <a class="code" href="../../d3/d63/classcv_1_1Mat.html">Mat</a> src = <a class="code" href="../../d4/da8/group__imgcodecs.html#ga288b8b3da0892bd651fce07b3bbd3a56">imread</a>( <a class="code" href="../../d6/dba/group__core__utils__samples.html#ga3a33b00033b46c698ff6340d95569c13">samples::findFile</a>( filename ), <a class="code" href="../../d8/d6a/group__imgcodecs__flags.html#gga61d9b0126a3e57d9277ac48327799c80ae29981cfc153d3b0cef5c0daeedd2125">IMREAD_GRAYSCALE</a> );</div><div class="line"></div><div class="line">    <span class="comment">// Check if image is loaded fine</span></div><div class="line">    <span class="keywordflow">if</span>(src.<a class="code" href="../../d3/d63/classcv_1_1Mat.html#abbec3525a852e77998aba034813fded4">empty</a>()){</div><div class="line">        printf(<span class="stringliteral">&quot; Error opening image\n&quot;</span>);</div><div class="line">        printf(<span class="stringliteral">&quot; Program Arguments: [image_name -- default %s] \n&quot;</span>, default_file);</div><div class="line">        <span class="keywordflow">return</span> -1;</div><div class="line">    }</div><div class="line"></div><div class="line">    <span class="comment">// Edge detection</span></div><div class="line">    <a class="code" href="../../dd/d1a/group__imgproc__feature.html#ga04723e007ed888ddf11d9ba04e2232de">Canny</a>(src, dst, 50, 200, 3);</div><div class="line"></div><div class="line">    <span class="comment">// Copy edges to the images that will display the results in BGR</span></div><div class="line">    <a class="code" href="../../d8/d01/group__imgproc__color__conversions.html#ga397ae87e1288a81d2363b61574eb8cab">cvtColor</a>(dst, cdst, <a class="code" href="../../d8/d01/group__imgproc__color__conversions.html#gga4e0972be5de079fed4e3a10e24ef5ef0a869da65c045477f2f17d39395df65b2d">COLOR_GRAY2BGR</a>);</div><div class="line">    cdstP = cdst.<a class="code" href="../../d3/d63/classcv_1_1Mat.html#adff2ea98da45eae0833e73582dd4a660">clone</a>();</div><div class="line"></div><div class="line">    <span class="comment">// Standard Hough Line Transform</span></div><div class="line">    vector&lt;Vec2f&gt; lines; <span class="comment">// will hold the results of the detection</span></div><div class="line">    <a class="code" href="../../dd/d1a/group__imgproc__feature.html#ga46b4e588934f6c8dfd509cc6e0e4545a">HoughLines</a>(dst, lines, 1, CV_PI/180, 150, 0, 0 ); <span class="comment">// runs the actual detection</span></div><div class="line"><span class="comment"></span>    <span class="comment">// Draw the lines</span></div><div class="line">    <span class="keywordflow">for</span>( <span class="keywordtype">size_t</span> i = 0; i &lt; lines.size(); i++ )</div><div class="line">    {</div><div class="line">        <span class="keywordtype">float</span> rho = lines[i][0], theta = lines[i][1];</div><div class="line">        <a class="code" href="../../db/d4e/classcv_1_1Point__.html">Point</a> pt1, pt2;</div><div class="line">        <span class="keywordtype">double</span> a = <a class="code" href="../../d0/de1/group__core.html#gaf0f2fe47183d063fb7415097fbadb570">cos</a>(theta), b = <a class="code" href="../../d0/de1/group__core.html#ga53a8656033a51db64caa72ee9d4e93b4">sin</a>(theta);</div><div class="line">        <span class="keywordtype">double</span> x0 = a*rho, y0 = b*rho;</div><div class="line">        pt1.<a class="code" href="../../db/d4e/classcv_1_1Point__.html#a4c96fa7bdbfe390be5ed356edb274ff3">x</a> = <a class="code" href="../../db/de0/group__core__utils.html#ga085eca238176984a0b72df2818598d85">cvRound</a>(x0 + 1000*(-b));</div><div class="line">        pt1.<a class="code" href="../../db/d4e/classcv_1_1Point__.html#a157337197338ff199e5df1a393022f15">y</a> = <a class="code" href="../../db/de0/group__core__utils.html#ga085eca238176984a0b72df2818598d85">cvRound</a>(y0 + 1000*(a));</div><div class="line">        pt2.<a class="code" href="../../db/d4e/classcv_1_1Point__.html#a4c96fa7bdbfe390be5ed356edb274ff3">x</a> = <a class="code" href="../../db/de0/group__core__utils.html#ga085eca238176984a0b72df2818598d85">cvRound</a>(x0 - 1000*(-b));</div><div class="line">        pt2.<a class="code" href="../../db/d4e/classcv_1_1Point__.html#a157337197338ff199e5df1a393022f15">y</a> = <a class="code" href="../../db/de0/group__core__utils.html#ga085eca238176984a0b72df2818598d85">cvRound</a>(y0 - 1000*(a));</div><div class="line">        <a class="code" href="../../d6/d6e/group__imgproc__draw.html#ga7078a9fae8c7e7d13d24dac2520ae4a2">line</a>( cdst, pt1, pt2, <a class="code" href="../../dc/d84/group__core__basic.html#ga599fe92e910c027be274233eccad7beb">Scalar</a>(0,0,255), 3, <a class="code" href="../../d6/d6e/group__imgproc__draw.html#ggaf076ef45de481ac96e0ab3dc2c29a777a85fdabe5335c9e6656563dfd7c94fb4f">LINE_AA</a>);</div><div class="line">    }</div><div class="line"></div><div class="line">    <span class="comment">// Probabilistic Line Transform</span></div><div class="line">    vector&lt;Vec4i&gt; linesP; <span class="comment">// will hold the results of the detection</span></div><div class="line">    <a class="code" href="../../dd/d1a/group__imgproc__feature.html#ga8618180a5948286384e3b7ca02f6feeb">HoughLinesP</a>(dst, linesP, 1, CV_PI/180, 50, 50, 10 ); <span class="comment">// runs the actual detection</span></div><div class="line"><span class="comment"></span>    <span class="comment">// Draw the lines</span></div><div class="line">    <span class="keywordflow">for</span>( <span class="keywordtype">size_t</span> i = 0; i &lt; linesP.size(); i++ )</div><div class="line">    {</div><div class="line">        <a class="code" href="../../d6/dcf/classcv_1_1Vec.html">Vec4i</a> l = linesP[i];</div><div class="line">        <a class="code" href="../../d6/d6e/group__imgproc__draw.html#ga7078a9fae8c7e7d13d24dac2520ae4a2">line</a>( cdstP, <a class="code" href="../../dc/d84/group__core__basic.html#ga1e83eafb2d26b3c93f09e8338bcab192">Point</a>(l[0], l[1]), <a class="code" href="../../dc/d84/group__core__basic.html#ga1e83eafb2d26b3c93f09e8338bcab192">Point</a>(l[2], l[3]), <a class="code" href="../../dc/d84/group__core__basic.html#ga599fe92e910c027be274233eccad7beb">Scalar</a>(0,0,255), 3, <a class="code" href="../../d6/d6e/group__imgproc__draw.html#ggaf076ef45de481ac96e0ab3dc2c29a777a85fdabe5335c9e6656563dfd7c94fb4f">LINE_AA</a>);</div><div class="line">    }</div><div class="line"></div><div class="line">    <span class="comment">// Show results</span></div><div class="line">    <a class="code" href="../../d7/dfc/group__highgui.html#ga453d42fe4cb60e5723281a89973ee563">imshow</a>(<span class="stringliteral">&quot;Source&quot;</span>, src);</div><div class="line">    <a class="code" href="../../d7/dfc/group__highgui.html#ga453d42fe4cb60e5723281a89973ee563">imshow</a>(<span class="stringliteral">&quot;Detected Lines (in red) - Standard Hough Line Transform&quot;</span>, cdst);</div><div class="line">    <a class="code" href="../../d7/dfc/group__highgui.html#ga453d42fe4cb60e5723281a89973ee563">imshow</a>(<span class="stringliteral">&quot;Detected Lines (in red) - Probabilistic Line Transform&quot;</span>, cdstP);</div><div class="line"></div><div class="line">    <span class="comment">// Wait and Exit</span></div><div class="line">    <a class="code" href="../../d7/dfc/group__highgui.html#ga5628525ad33f52eab17feebcfba38bd7">waitKey</a>();</div><div class="line">    <span class="keywordflow">return</span> 0;</div><div class="line">}</div></div><!-- fragment -->  </div>  <div class='newInnerHTML' title='java' style='display: none;'>Java</div><div class='toggleable_div label_java' style='display: none;'><p> The sample code that we will explain can be downloaded from <a href="https://raw.githubusercontent.com/opencv/opencv/master/samples/java/tutorial_code/ImgTrans/HoughLine/HoughLines.java">here</a>. </p><div class="fragment"><div class="line"></div><div class="line"><span class="keyword">import</span> org.opencv.core.*;</div><div class="line"><span class="keyword">import</span> org.opencv.core.Point;</div><div class="line"><span class="keyword">import</span> org.opencv.highgui.HighGui;</div><div class="line"><span class="keyword">import</span> org.opencv.imgcodecs.Imgcodecs;</div><div class="line"><span class="keyword">import</span> org.opencv.imgproc.Imgproc;</div><div class="line"></div><div class="line"><span class="keyword">class </span>HoughLinesRun {</div><div class="line"></div><div class="line">    <span class="keyword">public</span> <span class="keywordtype">void</span> run(<a class="code" href="../../dc/d84/group__core__basic.html#ga1f6634802eeadfd7245bc75cf3e216c2">String</a>[] args) {</div><div class="line">        <span class="comment">// Declare the output variables</span></div><div class="line">        Mat dst = <span class="keyword">new</span> Mat(), cdst = <span class="keyword">new</span> Mat(), cdstP;</div><div class="line"></div><div class="line">        <a class="code" href="../../dc/d84/group__core__basic.html#ga1f6634802eeadfd7245bc75cf3e216c2">String</a> default_file = <span class="stringliteral">&quot;../../../../data/sudoku.png&quot;</span>;</div><div class="line">        <a class="code" href="../../dc/d84/group__core__basic.html#ga1f6634802eeadfd7245bc75cf3e216c2">String</a> filename = ((args.length &gt; 0) ? args[0] : default_file);</div><div class="line"></div><div class="line">        <span class="comment">// Load an image</span></div><div class="line">        Mat src = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);</div><div class="line"></div><div class="line">        <span class="comment">// Check if image is loaded fine</span></div><div class="line">        <span class="keywordflow">if</span>( src.empty() ) {</div><div class="line">            System.out.println(<span class="stringliteral">&quot;Error opening image!&quot;</span>);</div><div class="line">            System.out.println(<span class="stringliteral">&quot;Program Arguments: [image_name -- default &quot;</span></div><div class="line">                    + default_file +<span class="stringliteral">&quot;] \n&quot;</span>);</div><div class="line">            System.exit(-1);</div><div class="line">        }</div><div class="line"></div><div class="line">        <span class="comment">// Edge detection</span></div><div class="line">        Imgproc.Canny(src, dst, 50, 200, 3, <span class="keyword">false</span>);</div><div class="line"></div><div class="line">        <span class="comment">// Copy edges to the images that will display the results in BGR</span></div><div class="line">        Imgproc.cvtColor(dst, cdst, Imgproc.COLOR_GRAY2BGR);</div><div class="line">        cdstP = cdst.<a class="code" href="../../d3/d63/classcv_1_1Mat.html#adff2ea98da45eae0833e73582dd4a660">clone</a>();</div><div class="line"></div><div class="line">        <span class="comment">// Standard Hough Line Transform</span></div><div class="line">        Mat lines = <span class="keyword">new</span> Mat(); <span class="comment">// will hold the results of the detection</span></div><div class="line">        Imgproc.HoughLines(dst, lines, 1, Math.PI/180, 150); <span class="comment">// runs the actual detection</span></div><div class="line"><span class="comment"></span>        <span class="comment">// Draw the lines</span></div><div class="line">        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> x = 0; x &lt; lines.rows(); x++) {</div><div class="line">            <span class="keywordtype">double</span> rho = lines.get(x, 0)[0],</div><div class="line">                    theta = lines.get(x, 0)[1];</div><div class="line"></div><div class="line">            <span class="keywordtype">double</span> a = Math.cos(theta), b = Math.sin(theta);</div><div class="line">            <span class="keywordtype">double</span> x0 = a*rho, y0 = b*rho;</div><div class="line">            <a class="code" href="../../dc/d84/group__core__basic.html#ga1e83eafb2d26b3c93f09e8338bcab192">Point</a> pt1 = <span class="keyword">new</span> <a class="code" href="../../dc/d84/group__core__basic.html#ga1e83eafb2d26b3c93f09e8338bcab192">Point</a>(Math.round(x0 + 1000*(-b)), Math.round(y0 + 1000*(a)));</div><div class="line">            <a class="code" href="../../dc/d84/group__core__basic.html#ga1e83eafb2d26b3c93f09e8338bcab192">Point</a> pt2 = <span class="keyword">new</span> <a class="code" href="../../dc/d84/group__core__basic.html#ga1e83eafb2d26b3c93f09e8338bcab192">Point</a>(Math.round(x0 - 1000*(-b)), Math.round(y0 - 1000*(a)));</div><div class="line">            Imgproc.line(cdst, pt1, pt2, <span class="keyword">new</span> <a class="code" href="../../dc/d84/group__core__basic.html#ga599fe92e910c027be274233eccad7beb">Scalar</a>(0, 0, 255), 3, Imgproc.LINE_AA, 0);</div><div class="line">        }</div><div class="line"></div><div class="line">        <span class="comment">// Probabilistic Line Transform</span></div><div class="line">        Mat linesP = <span class="keyword">new</span> Mat(); <span class="comment">// will hold the results of the detection</span></div><div class="line">        Imgproc.HoughLinesP(dst, linesP, 1, Math.PI/180, 50, 50, 10); <span class="comment">// runs the actual detection</span></div><div class="line"><span class="comment"></span>        <span class="comment">// Draw the lines</span></div><div class="line">        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> x = 0; x &lt; linesP.rows(); x++) {</div><div class="line">            <span class="keywordtype">double</span>[] l = linesP.get(x, 0);</div><div class="line">            Imgproc.line(cdstP, <span class="keyword">new</span> <a class="code" href="../../dc/d84/group__core__basic.html#ga1e83eafb2d26b3c93f09e8338bcab192">Point</a>(l[0], l[1]), <span class="keyword">new</span> <a class="code" href="../../dc/d84/group__core__basic.html#ga1e83eafb2d26b3c93f09e8338bcab192">Point</a>(l[2], l[3]), <span class="keyword">new</span> <a class="code" href="../../dc/d84/group__core__basic.html#ga599fe92e910c027be274233eccad7beb">Scalar</a>(0, 0, 255), 3, Imgproc.LINE_AA, 0);</div><div class="line">        }</div><div class="line"></div><div class="line">        <span class="comment">// Show results</span></div><div class="line">        HighGui.imshow(<span class="stringliteral">&quot;Source&quot;</span>, src);</div><div class="line">        HighGui.imshow(<span class="stringliteral">&quot;Detected Lines (in red) - Standard Hough Line Transform&quot;</span>, cdst);</div><div class="line">        HighGui.imshow(<span class="stringliteral">&quot;Detected Lines (in red) - Probabilistic Line Transform&quot;</span>, cdstP);</div><div class="line"></div><div class="line">        <span class="comment">// Wait and Exit</span></div><div class="line">        HighGui.waitKey();</div><div class="line">        System.exit(0);</div><div class="line">    }</div><div class="line">}</div><div class="line"></div><div class="line"><span class="keyword">public</span> <span class="keyword">class </span><a class="code" href="../../dd/d1a/group__imgproc__feature.html#ga46b4e588934f6c8dfd509cc6e0e4545a">HoughLines</a> {</div><div class="line">    <span class="keyword">public</span> <span class="keyword">static</span> <span class="keywordtype">void</span> main(<a class="code" href="../../dc/d84/group__core__basic.html#ga1f6634802eeadfd7245bc75cf3e216c2">String</a>[] args) {</div><div class="line">        <span class="comment">// Load the native library.</span></div><div class="line">        System.loadLibrary(Core.NATIVE_LIBRARY_NAME);</div><div class="line">        <span class="keyword">new</span> HoughLinesRun().run(args);</div><div class="line">    }</div><div class="line">}</div></div><!-- fragment -->  </div>  <div class='newInnerHTML' title='python' style='display: none;'>Python</div><div class='toggleable_div label_python' style='display: none;'><p> The sample code that we will explain can be downloaded from <a href="https://raw.githubusercontent.com/opencv/opencv/master/samples/python/tutorial_code/ImgTrans/HoughLine/hough_lines.py">here</a>. </p><div class="fragment"><div class="line"><span class="stringliteral">&quot;&quot;&quot;</span></div><div class="line"><span class="stringliteral">@file hough_lines.py</span></div><div class="line"><span class="stringliteral">@brief This program demonstrates line finding with the Hough transform</span></div><div class="line"><span class="stringliteral">&quot;&quot;&quot;</span></div><div class="line"><span class="keyword">import</span> sys</div><div class="line"><span class="keyword">import</span> math</div><div class="line"><span class="keyword">import</span> cv2 <span class="keyword">as</span> cv</div><div class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</div><div class="line"></div><div class="line"></div><div class="line"><span class="keyword">def </span>main(argv):</div><div class="line">    </div><div class="line">    default_file = <span class="stringliteral">&#39;sudoku.png&#39;</span></div><div class="line">    filename = argv[0] <span class="keywordflow">if</span> len(argv) &gt; 0 <span class="keywordflow">else</span> default_file</div><div class="line"></div><div class="line">    <span class="comment"># Loads an image</span></div><div class="line">    src = <a class="code" href="../../d4/da8/group__imgcodecs.html#ga288b8b3da0892bd651fce07b3bbd3a56">cv.imread</a>(<a class="code" href="../../d6/dba/group__core__utils__samples.html#ga3a33b00033b46c698ff6340d95569c13">cv.samples.findFile</a>(filename), cv.IMREAD_GRAYSCALE)</div><div class="line"></div><div class="line">    <span class="comment"># Check if image is loaded fine</span></div><div class="line">    <span class="keywordflow">if</span> src <span class="keywordflow">is</span> <span class="keywordtype">None</span>:</div><div class="line">        <span class="keywordflow">print</span> (<span class="stringliteral">&#39;Error opening image!&#39;</span>)</div><div class="line">        <span class="keywordflow">print</span> (<span class="stringliteral">&#39;Usage: hough_lines.py [image_name -- default &#39;</span> + default_file + <span class="stringliteral">&#39;] \n&#39;</span>)</div><div class="line">        <span class="keywordflow">return</span> -1</div><div class="line">    </div><div class="line"></div><div class="line">    </div><div class="line">    dst = <a class="code" href="../../dd/d1a/group__imgproc__feature.html#ga2a671611e104c093843d7b7fc46d24af">cv.Canny</a>(src, 50, 200, <span class="keywordtype">None</span>, 3)</div><div class="line">    </div><div class="line"></div><div class="line">    <span class="comment"># Copy edges to the images that will display the results in BGR</span></div><div class="line">    cdst = <a class="code" href="../../d8/d01/group__imgproc__color__conversions.html#ga397ae87e1288a81d2363b61574eb8cab">cv.cvtColor</a>(dst, cv.COLOR_GRAY2BGR)</div><div class="line">    cdstP = np.copy(cdst)</div><div class="line"></div><div class="line">    </div><div class="line">    lines = <a class="code" href="../../dd/d1a/group__imgproc__feature.html#ga46b4e588934f6c8dfd509cc6e0e4545a">cv.HoughLines</a>(dst, 1, np.pi / 180, 150, <span class="keywordtype">None</span>, 0, 0)</div><div class="line">    </div><div class="line">    <span class="keywordflow">if</span> lines <span class="keywordflow">is</span> <span class="keywordflow">not</span> <span class="keywordtype">None</span>:</div><div class="line">        <span class="keywordflow">for</span> i <span class="keywordflow">in</span> range(0, len(lines)):</div><div class="line">            rho = lines[i][0][0]</div><div class="line">            theta = lines[i][0][1]</div><div class="line">            a = math.cos(theta)</div><div class="line">            b = math.sin(theta)</div><div class="line">            x0 = a * rho</div><div class="line">            y0 = b * rho</div><div class="line">            pt1 = (int(x0 + 1000*(-b)), int(y0 + 1000*(a)))</div><div class="line">            pt2 = (int(x0 - 1000*(-b)), int(y0 - 1000*(a)))</div><div class="line"></div><div class="line">            <a class="code" href="../../d6/d6e/group__imgproc__draw.html#ga7078a9fae8c7e7d13d24dac2520ae4a2">cv.line</a>(cdst, pt1, pt2, (0,0,255), 3, cv.LINE_AA)</div><div class="line">    </div><div class="line"></div><div class="line">    </div><div class="line">    linesP = <a class="code" href="../../dd/d1a/group__imgproc__feature.html#ga8618180a5948286384e3b7ca02f6feeb">cv.HoughLinesP</a>(dst, 1, np.pi / 180, 50, <span class="keywordtype">None</span>, 50, 10)</div><div class="line">    </div><div class="line">    <span class="keywordflow">if</span> linesP <span class="keywordflow">is</span> <span class="keywordflow">not</span> <span class="keywordtype">None</span>:</div><div class="line">        <span class="keywordflow">for</span> i <span class="keywordflow">in</span> range(0, len(linesP)):</div><div class="line">            l = linesP[i][0]</div><div class="line">            <a class="code" href="../../d6/d6e/group__imgproc__draw.html#ga7078a9fae8c7e7d13d24dac2520ae4a2">cv.line</a>(cdstP, (l[0], l[1]), (l[2], l[3]), (0,0,255), 3, cv.LINE_AA)</div><div class="line">    </div><div class="line">    <a class="code" href="../../df/d24/group__highgui__opengl.html#gaae7e90aa3415c68dba22a5ff2cefc25d">cv.imshow</a>(<span class="stringliteral">&quot;Source&quot;</span>, src)</div><div class="line">    <a class="code" href="../../df/d24/group__highgui__opengl.html#gaae7e90aa3415c68dba22a5ff2cefc25d">cv.imshow</a>(<span class="stringliteral">&quot;Detected Lines (in red) - Standard Hough Line Transform&quot;</span>, cdst)</div><div class="line">    <a class="code" href="../../df/d24/group__highgui__opengl.html#gaae7e90aa3415c68dba22a5ff2cefc25d">cv.imshow</a>(<span class="stringliteral">&quot;Detected Lines (in red) - Probabilistic Line Transform&quot;</span>, cdstP)</div><div class="line">    </div><div class="line">    <a class="code" href="../../d7/dfc/group__highgui.html#ga5628525ad33f52eab17feebcfba38bd7">cv.waitKey</a>()</div><div class="line">    <span class="keywordflow">return</span> 0</div><div class="line">    </div><div class="line"></div><div class="line"><span class="keywordflow">if</span> __name__ == <span class="stringliteral">&quot;__main__&quot;</span>:</div><div class="line">    main(sys.argv[1:])</div></div><!-- fragment -->  </div> <h2>Explanation </h2>
<h4>Load an image:</h4>
 <div class='newInnerHTML' title='cpp' style='display: none;'>C++</div><div class='toggleable_div label_cpp' style='display: none;'> <div class="fragment"><div class="line">    <span class="keyword">const</span> <span class="keywordtype">char</span>* default_file = <span class="stringliteral">&quot;sudoku.png&quot;</span>;</div><div class="line">    <span class="keyword">const</span> <span class="keywordtype">char</span>* filename = argc &gt;=2 ? argv[1] : default_file;</div><div class="line"></div><div class="line">    <span class="comment">// Loads an image</span></div><div class="line">    Mat src = <a class="code" href="../../d4/da8/group__imgcodecs.html#ga288b8b3da0892bd651fce07b3bbd3a56">imread</a>( <a class="code" href="../../d6/dba/group__core__utils__samples.html#ga3a33b00033b46c698ff6340d95569c13">samples::findFile</a>( filename ), <a class="code" href="../../d8/d6a/group__imgcodecs__flags.html#gga61d9b0126a3e57d9277ac48327799c80ae29981cfc153d3b0cef5c0daeedd2125">IMREAD_GRAYSCALE</a> );</div><div class="line"></div><div class="line">    <span class="comment">// Check if image is loaded fine</span></div><div class="line">    <span class="keywordflow">if</span>(src.empty()){</div><div class="line">        printf(<span class="stringliteral">&quot; Error opening image\n&quot;</span>);</div><div class="line">        printf(<span class="stringliteral">&quot; Program Arguments: [image_name -- default %s] \n&quot;</span>, default_file);</div><div class="line">        <span class="keywordflow">return</span> -1;</div><div class="line">    }</div></div><!-- fragment --> </div>  <div class='newInnerHTML' title='java' style='display: none;'>Java</div><div class='toggleable_div label_java' style='display: none;'> <div class="fragment"><div class="line">        <a class="code" href="../../dc/d84/group__core__basic.html#ga1f6634802eeadfd7245bc75cf3e216c2">String</a> default_file = <span class="stringliteral">&quot;../../../../data/sudoku.png&quot;</span>;</div><div class="line">        <a class="code" href="../../dc/d84/group__core__basic.html#ga1f6634802eeadfd7245bc75cf3e216c2">String</a> filename = ((args.length &gt; 0) ? args[0] : default_file);</div><div class="line"></div><div class="line">        <span class="comment">// Load an image</span></div><div class="line">        Mat src = Imgcodecs.imread(filename, Imgcodecs.IMREAD_GRAYSCALE);</div><div class="line"></div><div class="line">        <span class="comment">// Check if image is loaded fine</span></div><div class="line">        <span class="keywordflow">if</span>( src.empty() ) {</div><div class="line">            System.out.println(<span class="stringliteral">&quot;Error opening image!&quot;</span>);</div><div class="line">            System.out.println(<span class="stringliteral">&quot;Program Arguments: [image_name -- default &quot;</span></div><div class="line">                    + default_file +<span class="stringliteral">&quot;] \n&quot;</span>);</div><div class="line">            System.exit(-1);</div><div class="line">        }</div></div><!-- fragment --> </div>  <div class='newInnerHTML' title='python' style='display: none;'>Python</div><div class='toggleable_div label_python' style='display: none;'> <div class="fragment"><div class="line">    default_file = <span class="stringliteral">&#39;sudoku.png&#39;</span></div><div class="line">    filename = argv[0] <span class="keywordflow">if</span> len(argv) &gt; 0 <span class="keywordflow">else</span> default_file</div><div class="line"></div><div class="line">    <span class="comment"># Loads an image</span></div><div class="line">    src = <a class="code" href="../../d4/da8/group__imgcodecs.html#ga288b8b3da0892bd651fce07b3bbd3a56">cv.imread</a>(<a class="code" href="../../d6/dba/group__core__utils__samples.html#ga3a33b00033b46c698ff6340d95569c13">cv.samples.findFile</a>(filename), cv.IMREAD_GRAYSCALE)</div><div class="line"></div><div class="line">    <span class="comment"># Check if image is loaded fine</span></div><div class="line">    <span class="keywordflow">if</span> src <span class="keywordflow">is</span> <span class="keywordtype">None</span>:</div><div class="line">        <span class="keywordflow">print</span> (<span class="stringliteral">&#39;Error opening image!&#39;</span>)</div><div class="line">        <span class="keywordflow">print</span> (<span class="stringliteral">&#39;Usage: hough_lines.py [image_name -- default &#39;</span> + default_file + <span class="stringliteral">&#39;] \n&#39;</span>)</div><div class="line">        <span class="keywordflow">return</span> -1</div></div><!-- fragment --> </div> <h4>Detect the edges of the image by using a Canny detector:</h4>
 <div class='newInnerHTML' title='cpp' style='display: none;'>C++</div><div class='toggleable_div label_cpp' style='display: none;'> <div class="fragment"><div class="line">    <span class="comment">// Edge detection</span></div><div class="line">    <a class="code" href="../../dd/d1a/group__imgproc__feature.html#ga04723e007ed888ddf11d9ba04e2232de">Canny</a>(src, dst, 50, 200, 3);</div></div><!-- fragment --> </div>  <div class='newInnerHTML' title='java' style='display: none;'>Java</div><div class='toggleable_div label_java' style='display: none;'> <div class="fragment"><div class="line">        <span class="comment">// Edge detection</span></div><div class="line">        Imgproc.Canny(src, dst, 50, 200, 3, <span class="keyword">false</span>);</div></div><!-- fragment --> </div>  <div class='newInnerHTML' title='python' style='display: none;'>Python</div><div class='toggleable_div label_python' style='display: none;'> <div class="fragment"><div class="line">    <span class="comment"># Edge detection</span></div><div class="line">    dst = <a class="code" href="../../dd/d1a/group__imgproc__feature.html#ga2a671611e104c093843d7b7fc46d24af">cv.Canny</a>(src, 50, 200, <span class="keywordtype">None</span>, 3)</div></div><!-- fragment --> </div> <p>Now we will apply the Hough Line Transform. We will explain how to use both OpenCV functions available for this purpose.</p>
<h4>Standard Hough Line Transform:</h4>
<p>First, you apply the Transform:</p>
 <div class='newInnerHTML' title='cpp' style='display: none;'>C++</div><div class='toggleable_div label_cpp' style='display: none;'> <div class="fragment"><div class="line">    <span class="comment">// Standard Hough Line Transform</span></div><div class="line">    vector&lt;Vec2f&gt; lines; <span class="comment">// will hold the results of the detection</span></div><div class="line">    <a class="code" href="../../dd/d1a/group__imgproc__feature.html#ga46b4e588934f6c8dfd509cc6e0e4545a">HoughLines</a>(dst, lines, 1, CV_PI/180, 150, 0, 0 ); <span class="comment">// runs the actual detection</span></div></div><!-- fragment --> </div>  <div class='newInnerHTML' title='java' style='display: none;'>Java</div><div class='toggleable_div label_java' style='display: none;'> <div class="fragment"><div class="line">        <span class="comment">// Standard Hough Line Transform</span></div><div class="line">        Mat lines = <span class="keyword">new</span> Mat(); <span class="comment">// will hold the results of the detection</span></div><div class="line">        Imgproc.HoughLines(dst, lines, 1, Math.PI/180, 150); <span class="comment">// runs the actual detection</span></div></div><!-- fragment --> </div>  <div class='newInnerHTML' title='python' style='display: none;'>Python</div><div class='toggleable_div label_python' style='display: none;'> <div class="fragment"><div class="line">    <span class="comment">#  Standard Hough Line Transform</span></div><div class="line">    lines = <a class="code" href="../../dd/d1a/group__imgproc__feature.html#ga46b4e588934f6c8dfd509cc6e0e4545a">cv.HoughLines</a>(dst, 1, np.pi / 180, 150, <span class="keywordtype">None</span>, 0, 0)</div></div><!-- fragment --> </div> <ul>
<li>with the following arguments:<ul>
<li><em>dst</em>: Output of the edge detector. It should be a grayscale image (although in fact it is a binary one)</li>
<li><em>lines</em>: A vector that will store the parameters \((r,\theta)\) of the detected lines</li>
<li><em>rho</em> : The resolution of the parameter \(r\) in pixels. We use <b>1</b> pixel.</li>
<li><em>theta</em>: The resolution of the parameter \(\theta\) in radians. We use <b>1 degree</b> (CV_PI/180)</li>
<li><em>threshold</em>: The minimum number of intersections to "*detect*" a line</li>
<li><em>srn</em> and <em>stn</em>: Default parameters to zero. Check OpenCV reference for more info.</li>
</ul>
</li>
</ul>
<p>And then you display the result by drawing the lines. </p> <div class='newInnerHTML' title='cpp' style='display: none;'>C++</div><div class='toggleable_div label_cpp' style='display: none;'> <div class="fragment"><div class="line">    <span class="comment">// Draw the lines</span></div><div class="line">    <span class="keywordflow">for</span>( <span class="keywordtype">size_t</span> i = 0; i &lt; lines.size(); i++ )</div><div class="line">    {</div><div class="line">        <span class="keywordtype">float</span> rho = lines[i][0], theta = lines[i][1];</div><div class="line">        <a class="code" href="../../dc/d84/group__core__basic.html#ga1e83eafb2d26b3c93f09e8338bcab192">Point</a> pt1, pt2;</div><div class="line">        <span class="keywordtype">double</span> a = <a class="code" href="../../d0/de1/group__core.html#gaf0f2fe47183d063fb7415097fbadb570">cos</a>(theta), b = <a class="code" href="../../d0/de1/group__core.html#ga53a8656033a51db64caa72ee9d4e93b4">sin</a>(theta);</div><div class="line">        <span class="keywordtype">double</span> x0 = a*rho, y0 = b*rho;</div><div class="line">        pt1.x = <a class="code" href="../../db/de0/group__core__utils.html#ga085eca238176984a0b72df2818598d85">cvRound</a>(x0 + 1000*(-b));</div><div class="line">        pt1.y = <a class="code" href="../../db/de0/group__core__utils.html#ga085eca238176984a0b72df2818598d85">cvRound</a>(y0 + 1000*(a));</div><div class="line">        pt2.x = <a class="code" href="../../db/de0/group__core__utils.html#ga085eca238176984a0b72df2818598d85">cvRound</a>(x0 - 1000*(-b));</div><div class="line">        pt2.y = <a class="code" href="../../db/de0/group__core__utils.html#ga085eca238176984a0b72df2818598d85">cvRound</a>(y0 - 1000*(a));</div><div class="line">        <a class="code" href="../../d6/d6e/group__imgproc__draw.html#ga7078a9fae8c7e7d13d24dac2520ae4a2">line</a>( cdst, pt1, pt2, <a class="code" href="../../dc/d84/group__core__basic.html#ga599fe92e910c027be274233eccad7beb">Scalar</a>(0,0,255), 3, <a class="code" href="../../d6/d6e/group__imgproc__draw.html#ggaf076ef45de481ac96e0ab3dc2c29a777a85fdabe5335c9e6656563dfd7c94fb4f">LINE_AA</a>);</div><div class="line">    }</div></div><!-- fragment --> </div>  <div class='newInnerHTML' title='java' style='display: none;'>Java</div><div class='toggleable_div label_java' style='display: none;'> <div class="fragment"><div class="line">        <span class="comment">// Draw the lines</span></div><div class="line">        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> x = 0; x &lt; lines.rows(); x++) {</div><div class="line">            <span class="keywordtype">double</span> rho = lines.get(x, 0)[0],</div><div class="line">                    theta = lines.get(x, 0)[1];</div><div class="line"></div><div class="line">            <span class="keywordtype">double</span> a = Math.cos(theta), b = Math.sin(theta);</div><div class="line">            <span class="keywordtype">double</span> x0 = a*rho, y0 = b*rho;</div><div class="line">            <a class="code" href="../../dc/d84/group__core__basic.html#ga1e83eafb2d26b3c93f09e8338bcab192">Point</a> pt1 = <span class="keyword">new</span> <a class="code" href="../../dc/d84/group__core__basic.html#ga1e83eafb2d26b3c93f09e8338bcab192">Point</a>(Math.round(x0 + 1000*(-b)), Math.round(y0 + 1000*(a)));</div><div class="line">            <a class="code" href="../../dc/d84/group__core__basic.html#ga1e83eafb2d26b3c93f09e8338bcab192">Point</a> pt2 = <span class="keyword">new</span> <a class="code" href="../../dc/d84/group__core__basic.html#ga1e83eafb2d26b3c93f09e8338bcab192">Point</a>(Math.round(x0 - 1000*(-b)), Math.round(y0 - 1000*(a)));</div><div class="line">            Imgproc.line(cdst, pt1, pt2, <span class="keyword">new</span> <a class="code" href="../../dc/d84/group__core__basic.html#ga599fe92e910c027be274233eccad7beb">Scalar</a>(0, 0, 255), 3, Imgproc.LINE_AA, 0);</div><div class="line">        }</div></div><!-- fragment --> </div>  <div class='newInnerHTML' title='python' style='display: none;'>Python</div><div class='toggleable_div label_python' style='display: none;'> <div class="fragment"><div class="line">    <span class="comment"># Draw the lines</span></div><div class="line">    <span class="keywordflow">if</span> lines <span class="keywordflow">is</span> <span class="keywordflow">not</span> <span class="keywordtype">None</span>:</div><div class="line">        <span class="keywordflow">for</span> i <span class="keywordflow">in</span> range(0, len(lines)):</div><div class="line">            rho = lines[i][0][0]</div><div class="line">            theta = lines[i][0][1]</div><div class="line">            a = math.cos(theta)</div><div class="line">            b = math.sin(theta)</div><div class="line">            x0 = a * rho</div><div class="line">            y0 = b * rho</div><div class="line">            pt1 = (int(x0 + 1000*(-b)), int(y0 + 1000*(a)))</div><div class="line">            pt2 = (int(x0 - 1000*(-b)), int(y0 - 1000*(a)))</div><div class="line"></div><div class="line">            <a class="code" href="../../d6/d6e/group__imgproc__draw.html#ga7078a9fae8c7e7d13d24dac2520ae4a2">cv.line</a>(cdst, pt1, pt2, (0,0,255), 3, cv.LINE_AA)</div></div><!-- fragment --> </div> <h4>Probabilistic Hough Line Transform</h4>
<p>First you apply the transform:</p>
 <div class='newInnerHTML' title='cpp' style='display: none;'>C++</div><div class='toggleable_div label_cpp' style='display: none;'> <div class="fragment"><div class="line">    <span class="comment">// Probabilistic Line Transform</span></div><div class="line">    vector&lt;Vec4i&gt; linesP; <span class="comment">// will hold the results of the detection</span></div><div class="line">    <a class="code" href="../../dd/d1a/group__imgproc__feature.html#ga8618180a5948286384e3b7ca02f6feeb">HoughLinesP</a>(dst, linesP, 1, CV_PI/180, 50, 50, 10 ); <span class="comment">// runs the actual detection</span></div></div><!-- fragment --> </div>  <div class='newInnerHTML' title='java' style='display: none;'>Java</div><div class='toggleable_div label_java' style='display: none;'> <div class="fragment"><div class="line">        <span class="comment">// Probabilistic Line Transform</span></div><div class="line">        Mat linesP = <span class="keyword">new</span> Mat(); <span class="comment">// will hold the results of the detection</span></div><div class="line">        Imgproc.HoughLinesP(dst, linesP, 1, Math.PI/180, 50, 50, 10); <span class="comment">// runs the actual detection</span></div></div><!-- fragment --> </div>  <div class='newInnerHTML' title='python' style='display: none;'>Python</div><div class='toggleable_div label_python' style='display: none;'> <div class="fragment"><div class="line">    <span class="comment"># Probabilistic Line Transform</span></div><div class="line">    linesP = <a class="code" href="../../dd/d1a/group__imgproc__feature.html#ga8618180a5948286384e3b7ca02f6feeb">cv.HoughLinesP</a>(dst, 1, np.pi / 180, 50, <span class="keywordtype">None</span>, 50, 10)</div></div><!-- fragment --> </div> <ul>
<li>with the arguments:<ul>
<li><em>dst</em>: Output of the edge detector. It should be a grayscale image (although in fact it is a binary one)</li>
<li><em>lines</em>: A vector that will store the parameters \((x_{start}, y_{start}, x_{end}, y_{end})\) of the detected lines</li>
<li><em>rho</em> : The resolution of the parameter \(r\) in pixels. We use <b>1</b> pixel.</li>
<li><em>theta</em>: The resolution of the parameter \(\theta\) in radians. We use <b>1 degree</b> (CV_PI/180)</li>
<li><em>threshold</em>: The minimum number of intersections to "*detect*" a line</li>
<li><em>minLineLength</em>: The minimum number of points that can form a line. Lines with less than this number of points are disregarded.</li>
<li><em>maxLineGap</em>: The maximum gap between two points to be considered in the same line.</li>
</ul>
</li>
</ul>
<p>And then you display the result by drawing the lines.</p>
 <div class='newInnerHTML' title='cpp' style='display: none;'>C++</div><div class='toggleable_div label_cpp' style='display: none;'> <div class="fragment"><div class="line">    <span class="comment">// Draw the lines</span></div><div class="line">    <span class="keywordflow">for</span>( <span class="keywordtype">size_t</span> i = 0; i &lt; linesP.size(); i++ )</div><div class="line">    {</div><div class="line">        <a class="code" href="../../dc/d84/group__core__basic.html#ga94ce799099ae6cdd66685e3fd0cad7d7">Vec4i</a> l = linesP[i];</div><div class="line">        <a class="code" href="../../d6/d6e/group__imgproc__draw.html#ga7078a9fae8c7e7d13d24dac2520ae4a2">line</a>( cdstP, <a class="code" href="../../dc/d84/group__core__basic.html#ga1e83eafb2d26b3c93f09e8338bcab192">Point</a>(l[0], l[1]), <a class="code" href="../../dc/d84/group__core__basic.html#ga1e83eafb2d26b3c93f09e8338bcab192">Point</a>(l[2], l[3]), <a class="code" href="../../dc/d84/group__core__basic.html#ga599fe92e910c027be274233eccad7beb">Scalar</a>(0,0,255), 3, <a class="code" href="../../d6/d6e/group__imgproc__draw.html#ggaf076ef45de481ac96e0ab3dc2c29a777a85fdabe5335c9e6656563dfd7c94fb4f">LINE_AA</a>);</div><div class="line">    }</div></div><!-- fragment --> </div>  <div class='newInnerHTML' title='java' style='display: none;'>Java</div><div class='toggleable_div label_java' style='display: none;'> <div class="fragment"><div class="line">        <span class="comment">// Draw the lines</span></div><div class="line">        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> x = 0; x &lt; linesP.rows(); x++) {</div><div class="line">            <span class="keywordtype">double</span>[] l = linesP.get(x, 0);</div><div class="line">            Imgproc.line(cdstP, <span class="keyword">new</span> <a class="code" href="../../dc/d84/group__core__basic.html#ga1e83eafb2d26b3c93f09e8338bcab192">Point</a>(l[0], l[1]), <span class="keyword">new</span> <a class="code" href="../../dc/d84/group__core__basic.html#ga1e83eafb2d26b3c93f09e8338bcab192">Point</a>(l[2], l[3]), <span class="keyword">new</span> <a class="code" href="../../dc/d84/group__core__basic.html#ga599fe92e910c027be274233eccad7beb">Scalar</a>(0, 0, 255), 3, Imgproc.LINE_AA, 0);</div><div class="line">        }</div></div><!-- fragment --> </div>  <div class='newInnerHTML' title='python' style='display: none;'>Python</div><div class='toggleable_div label_python' style='display: none;'> <div class="fragment"><div class="line">    <span class="comment"># Draw the lines</span></div><div class="line">    <span class="keywordflow">if</span> linesP <span class="keywordflow">is</span> <span class="keywordflow">not</span> <span class="keywordtype">None</span>:</div><div class="line">        <span class="keywordflow">for</span> i <span class="keywordflow">in</span> range(0, len(linesP)):</div><div class="line">            l = linesP[i][0]</div><div class="line">            <a class="code" href="../../d6/d6e/group__imgproc__draw.html#ga7078a9fae8c7e7d13d24dac2520ae4a2">cv.line</a>(cdstP, (l[0], l[1]), (l[2], l[3]), (0,0,255), 3, cv.LINE_AA)</div></div><!-- fragment --> </div> <h4>Display the original image and the detected lines:</h4>
 <div class='newInnerHTML' title='cpp' style='display: none;'>C++</div><div class='toggleable_div label_cpp' style='display: none;'> <div class="fragment"><div class="line">    <span class="comment">// Show results</span></div><div class="line">    <a class="code" href="../../d7/dfc/group__highgui.html#ga453d42fe4cb60e5723281a89973ee563">imshow</a>(<span class="stringliteral">&quot;Source&quot;</span>, src);</div><div class="line">    <a class="code" href="../../d7/dfc/group__highgui.html#ga453d42fe4cb60e5723281a89973ee563">imshow</a>(<span class="stringliteral">&quot;Detected Lines (in red) - Standard Hough Line Transform&quot;</span>, cdst);</div><div class="line">    <a class="code" href="../../d7/dfc/group__highgui.html#ga453d42fe4cb60e5723281a89973ee563">imshow</a>(<span class="stringliteral">&quot;Detected Lines (in red) - Probabilistic Line Transform&quot;</span>, cdstP);</div></div><!-- fragment --> </div>  <div class='newInnerHTML' title='java' style='display: none;'>Java</div><div class='toggleable_div label_java' style='display: none;'> <div class="fragment"><div class="line">        <span class="comment">// Show results</span></div><div class="line">        HighGui.imshow(<span class="stringliteral">&quot;Source&quot;</span>, src);</div><div class="line">        HighGui.imshow(<span class="stringliteral">&quot;Detected Lines (in red) - Standard Hough Line Transform&quot;</span>, cdst);</div><div class="line">        HighGui.imshow(<span class="stringliteral">&quot;Detected Lines (in red) - Probabilistic Line Transform&quot;</span>, cdstP);</div></div><!-- fragment --> </div>  <div class='newInnerHTML' title='python' style='display: none;'>Python</div><div class='toggleable_div label_python' style='display: none;'> <div class="fragment"><div class="line">    <span class="comment"># Show results</span></div><div class="line">    <a class="code" href="../../df/d24/group__highgui__opengl.html#gaae7e90aa3415c68dba22a5ff2cefc25d">cv.imshow</a>(<span class="stringliteral">&quot;Source&quot;</span>, src)</div><div class="line">    <a class="code" href="../../df/d24/group__highgui__opengl.html#gaae7e90aa3415c68dba22a5ff2cefc25d">cv.imshow</a>(<span class="stringliteral">&quot;Detected Lines (in red) - Standard Hough Line Transform&quot;</span>, cdst)</div><div class="line">    <a class="code" href="../../df/d24/group__highgui__opengl.html#gaae7e90aa3415c68dba22a5ff2cefc25d">cv.imshow</a>(<span class="stringliteral">&quot;Detected Lines (in red) - Probabilistic Line Transform&quot;</span>, cdstP)</div></div><!-- fragment --> </div> <h4>Wait until the user exits the program</h4>
 <div class='newInnerHTML' title='cpp' style='display: none;'>C++</div><div class='toggleable_div label_cpp' style='display: none;'> <div class="fragment"><div class="line">    <span class="comment">// Wait and Exit</span></div><div class="line">    <a class="code" href="../../d7/dfc/group__highgui.html#ga5628525ad33f52eab17feebcfba38bd7">waitKey</a>();</div><div class="line">    <span class="keywordflow">return</span> 0;</div></div><!-- fragment --> </div>  <div class='newInnerHTML' title='java' style='display: none;'>Java</div><div class='toggleable_div label_java' style='display: none;'> <div class="fragment"><div class="line">        <span class="comment">// Wait and Exit</span></div><div class="line">        HighGui.waitKey();</div><div class="line">        System.exit(0);</div></div><!-- fragment --> </div>  <div class='newInnerHTML' title='python' style='display: none;'>Python</div><div class='toggleable_div label_python' style='display: none;'> <div class="fragment"><div class="line">    <span class="comment"># Wait and Exit</span></div><div class="line">    <a class="code" href="../../d7/dfc/group__highgui.html#ga5628525ad33f52eab17feebcfba38bd7">cv.waitKey</a>()</div><div class="line">    <span class="keywordflow">return</span> 0</div></div><!-- fragment --> </div> <h2>Result </h2>
<dl class="section note"><dt>Note</dt><dd>The results below are obtained using the slightly fancier version we mentioned in the <em>Code</em> section. It still implements the same stuff as above, only adding the Trackbar for the Threshold.</dd></dl>
<p>Using an input image such as a <a href="https://raw.githubusercontent.com/opencv/opencv/master/samples/data/sudoku.png">sudoku image</a>. We get the following result by using the Standard Hough Line Transform: </p><div class="image">
<img src="../../hough_lines_result1.png" alt="hough_lines_result1.png"/>
</div>
<p> And by using the Probabilistic Hough Line Transform: </p><div class="image">
<img src="../../hough_lines_result2.png" alt="hough_lines_result2.png"/>
</div>
<p>You may observe that the number of lines detected vary while you change the <em>threshold</em>. The explanation is sort of evident: If you establish a higher threshold, fewer lines will be detected (since you will need more points to declare a line detected). </p>
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